|Table of Contents|

Damage diagnosis of complex structure based on convolution neural network and multi-label classification(PDF)

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

Issue:
2025年01期
Page:
101-111
Research Field:
建筑结构
Publishing date:

Info

Title:
Damage diagnosis of complex structure based on convolution neural network and multi-label classification
Author(s):
LI Shujin1;  YANG Fanfan1;  ZHANG Yuanjin2
(1. School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, Hubei, China; 2. School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, Hubei, China)
Keywords:
damage diagnosis;  convolution neural network;  multi-label classification;  frame structure;  deep learning
PACS:
TU312.3
DOI:
10.19815/j.jace.2023.02027
Abstract:
In order to study the damage diagnosis of complex spatial frame joints as the research object, two convolutional neural network models of multi-label single-output and multi-label multi-output were constructed by using the advantages of multi-label classification, which were used to judge the damage location and damage degree of frame structure joints. Aiming at the problems of multiple location conditions and low recognition accuracy of damage joints in complex structures, a multi-label multi-output convolutional neural network model was proposed, which could process the structure hierarchically(or partitioned)and complete the damage diagnosis at the same time. The shallow, deep and deep residual multi-output convolution neural network models for multi-label classification were constructed respectively, and their generalization performance was studied. The results show that the proposed model has high damage diagnosis accuracy and certain anti-noise performance. In particular, the multi-label multi-output network model after hierarchical(or partition)processing is more efficient, with faster convergence speed and higher diagnostic accuracy. Using the multi-label multi-output residual convolution neural network model, enough damage information can be extracted from the dynamic response, and the damage level of each joint can be accurately determined in the face of unlearned conditions.

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Last Update: 2025-01-20